Table of Content

Advances on Modeling and State Estimation for Industrial Processes

Submission Deadline: 30 November 2021 (closed)

Guest Editors

Prof. Shunyi Zhao, Jiangnan University, Wuxi, China
Prof. Xiaoli Luan, Jiangnan University, Wuxi, China
Prof. Jinfeng Liu, University of Alberta, Edmonton, Canada
Dr. Ruomu Tan, ABB Corporate Research Germany, Ladenburg, Germany

Summary

In the past few years, significant progress has been made in modeling and state estimation for industrial processes to improve control performance, reliable monitoring, quick and accurate fault detection, diagnosis,  high product quality,  fule and resource consumption, etc. However, with the fast development of information technology, numerous essential issues are facing in modeling and state estimation, which generates the new need for novel modeling and or state estimation methodologies and in-depth studies of them.

 

For example, due to many online sensors equipped, measurements are commonly collected with the characterizes of high volume, velocity, and variety. Therefore, feature selection or dimension reduction that extracts the signal interested and removes redundant information will be more critical during modeling than before, which will, in turn, affect the modeling and estimation algorithmic performance. Fast operation and high computational efficiency of an algorithm matter more since data often arrive rapidly, and the target values are usually required in an online manner. Besides, high accurate estimates of key parameters become more critical since reliable and precise modeling plays a vital role in many tasks, including control, detection, and monitoring. All these challenge the current statistical signal processing techniques from applicability, computational efficiency, and effectiveness. This special issue aims to bring the researchers in this area together with the engineers to break down barriers and develop innovative solutions and practical algorithms.

 

Potential topics include but are not limited to the following:

 

● Variational Bayesian Modeling Methods for Industrial Process

● Transfer Modeling for Industrial Process

● Unsupervised Modeling for Industrial Process

● First Principle Modeling for Industrial Process 

● Non-parametric Bayesian Modeling for Industrial Process

● Distributed Multi-Agent Modeling Algorithms and Its Industrial Applications

● Robust Modeling Methods for Industrial Process

● Supervised Modeling and Its Industrial Applications

● Filter-Aided Methods for Industrial Processes

● Nonlinear Modeling Methods and Its Industrial Applications


Keywords

• First Principal Modeling
• Unsupervised Modeling
• Non-parametric Bayesian Modeling
• Robust Modeling
• Filter-Aided Methods
• Nonlinear Modeling Methods
• Industrial Process

Published Papers


  • Open Access

    ARTICLE

    Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

    Rebecca Gedda, Larisa Beilina, Ruomu Tan
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1737-1759, 2023, DOI:10.32604/cmes.2022.019764
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a cost function with certain… More >

    Graphic Abstract

    Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

  • Open Access

    ARTICLE

    Improved Adaptive Iterated Extended Kalman Filter for GNSS/INS/UWB-Integrated Fixed-Point Positioning

    Qingdong Wu, Chenxi Li, Tao Shen, Yuan Xu
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1761-1772, 2023, DOI:10.32604/cmes.2022.020545
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract To provide stable and accurate position information of control points in a complex coastal environment, an adaptive iterated extended Kalman filter (AIEKF) for fixed-point positioning integrating global navigation satellite system, inertial navigation system, and ultra wide band (UWB) is proposed. In this method, the switched global navigation satellite system (GNSS) and UWB measurement are used as the measurement of the proposed filter. For the data fusion filter, the expectation-maximization (EM) based IEKF is used as the forward filter, then, the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing. Tests illustrate that the proposed AIEKF is able to provide an accurate estimation. More >

  • Open Access

    ARTICLE

    Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks

    Huiwen Xue, Jiwei Wen, Akshya Kumar Swain, Xiaoli Luan
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 857-871, 2023, DOI:10.32604/cmes.2022.020127
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently. Therefore,… More >

  • Open Access

    ARTICLE

    State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

    Wentao Liu, Junxia Ma, Weili Xiong
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 873-892, 2023, DOI:10.32604/cmes.2022.020565
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance. Furthermore, to improve the performance of… More >

  • Open Access

    ARTICLE

    Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence

    Zhiguo Wang, Fangqing Gao, Fei Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 343-355, 2023, DOI:10.32604/cmes.2022.020569
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative (PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the maximum tracking error is not limited by the initial value of PPD. The convergence… More >

  • Open Access

    ARTICLE

    Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder

    Jinfu Wang, Shunyi Zhao, Fei Liu, Zhenyi Ma
    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 531-552, 2022, DOI:10.32604/cmes.2022.019521
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract In modern industry, process monitoring plays a significant role in improving the quality of process conduct. With the higher dimensional of the industrial data, the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database. Nevertheless, these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices, especially the T2 on them. Variational AutoEncoders (VAE), an unsupervised deep learning algorithm using the hierarchy study method, has the ability to make the latent variables follow the Gaussian distribution. The partial least squares… More >

  • Open Access

    ARTICLE

    Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference

    Chen Xu, Yawen Mao, Hongtian Chen, Hongfeng Tao, Fei Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 349-364, 2022, DOI:10.32604/cmes.2021.019027
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise. Based on the cubature Kalman filter, we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise. The system states and the statistics of skew t noise distribution, including the shape matrix, the scale matrix, and the degree of freedom (DOF) are estimated jointly by employing variational Bayesian (VB) inference. The proposed method is validated in a target tracking example. Results of the simulation indicate that the proposed nonlinear filter can perform… More >

  • Open Access

    ARTICLE

    A Novel Bidirectional Interaction Model and Electric Energy Measuring Scheme of EVs for V2G with Distorted Power Loads

    Jiarui Cui, Qing Li, Bin Cao, Xiangquan Li, Qun Yan
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1789-1806, 2022, DOI:10.32604/cmes.2022.017958
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract With the increasing demand for petroleum resources and environmental issues, new energy electric vehicles are increasingly being used. However, the large number of electric vehicles connected to the grid has brought new challenges to the operation of the grid. Firstly, A novel bidirectional interaction model is established based on modulation theory with nonlinear loads. Then, the electric energy measuring scheme of EVs for V2G is derived under the conditions of distorted power loads. The scheme is composed of fundamental electric energy, fundamental-distorted electric energy, distorted-fundamental electric energy and distorted electric energy. And the characteristics of each electric energy are analyzed.… More >

  • Open Access

    ARTICLE

    Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

    Xiangquan Li, Zhengguang Xu, Cheng Han, Ning Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1807-1825, 2022, DOI:10.32604/cmes.2022.017799
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm (M-AM-SGRPIA) for a class of single input single output (SISO) linear output error models with multi-threshold quantized observations. It proves the convergence of the designed algorithm. A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output (SIMO) or SISO nonlinear systems, and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system. The system input design is accomplished using the measurement technology of random repeatability test, and the probabilistic characteristic… More >

  • Open Access

    ARTICLE

    Range-Only UWB SLAM for Indoor Robot Localization Employing Multi-Interval EFIR Rauch-Tung-Striebel Smoother

    Yanli Gao, Wanfeng Ma, Jing Cao, Jianling Qu and Yuan Xu
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1221-1237, 2022, DOI:10.32604/cmes.2022.017533
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract For improving the localization accuracy, a multi-interval extended finite impulse response (EFIR)-based RauchTung-Striebel (R-T-S) smoother is proposed for the range-only ultra wide band (UWB) simultaneous localization and mapping (SLAM) for robot localization. In this mode, the EFIR R-T-S (ERTS) smoother employs EFIR filter as the forward filter and the R-T-S smoothing method to smooth the EFIR filter’s output. When the east or the north position is considered as stance, the ERTS is used to smooth the position directly. Moreover, the estimation of the UWB Reference Nodes’ (RNs’) position is smoothed by the R-T-S smooth method in parallel. The test illustrates… More >

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